IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v199y2009i2p409-419.html
   My bibliography  Save this article

Supply chain design under uncertainty using sample average approximation and dual decomposition

Author

Listed:
  • Schütz, Peter
  • Tomasgard, Asgeir
  • Ahmed, Shabbir

Abstract

We present a supply chain design problem modeled as a sequence of splitting and combining processes. We formulate the problem as a two-stage stochastic program. The first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions. The objective is to minimize the sum of investment costs and expected costs of operating the supply chain. In particular the model emphasizes the importance of operational flexibility when making strategic decisions. For that reason short-term uncertainty is considered as well as long-term uncertainty. The real-world case used to illustrate the model is from the Norwegian meat industry. We solve the problem by sample average approximation in combination with dual decomposition. Computational results are presented for different sample sizes and different levels of data aggregation in the second stage.

Suggested Citation

  • Schütz, Peter & Tomasgard, Asgeir & Ahmed, Shabbir, 2009. "Supply chain design under uncertainty using sample average approximation and dual decomposition," European Journal of Operational Research, Elsevier, vol. 199(2), pages 409-419, December.
    • Handle: RePEc:eee:ejores:v:199:y:2009:i:2:p:409-419
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(08)01031-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. A. M. Geoffrion & G. W. Graves, 1974. "Multicommodity Distribution System Design by Benders Decomposition," Management Science, INFORMS, vol. 20(5), pages 822-844, January.
    4. C Lucas & S A MirHassani & G Mitra & C A Poojari, 2001. "An application of Lagrangian relaxation to a capacity planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(11), pages 1256-1266, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ragheb Rahmaniani & Shabbir Ahmed & Teodor Gabriel Crainic & Michel Gendreau & Walter Rei, 2020. "The Benders Dual Decomposition Method," Operations Research, INFORMS, vol. 68(3), pages 878-895, May.
    2. Huang, Edward & Mital, Pratik & Goetschalckx, Marc & Wu, Kan, 2016. "Optimal assignment of airport baggage unloading zones to outgoing flights," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 110-122.
    3. Maher, Stephen J., 2021. "Implementing the branch-and-cut approach for a general purpose Benders’ decomposition framework," European Journal of Operational Research, Elsevier, vol. 290(2), pages 479-498.
    4. M. Fonseca & Álvaro García-Sánchez & Miguel Ortega-Mier & Francisco Saldanha-da-Gama, 2010. "A stochastic bi-objective location model for strategic reverse logistics," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 158-184, July.
    5. Šárka Štádlerová & Sanjay Dominik Jena & Peter Schütz, 2023. "Using Lagrangian relaxation to locate hydrogen production facilities under uncertain demand: a case study from Norway," Computational Management Science, Springer, vol. 20(1), pages 1-32, December.
    6. Pilla, Venkata L. & Rosenberger, Jay M. & Chen, Victoria & Engsuwan, Narakorn & Siddappa, Sheela, 2012. "A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model," European Journal of Operational Research, Elsevier, vol. 216(1), pages 162-171.
    7. Aydin, Nezir & Murat, Alper, 2013. "A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem," International Journal of Production Economics, Elsevier, vol. 145(1), pages 173-183.
    8. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    9. Alzaman, Chaher & Zhang, Zhi-Hai & Diabat, Ali, 2018. "Supply chain network design with direct and indirect production costs: Hybrid gradient and local search based heuristics," International Journal of Production Economics, Elsevier, vol. 203(C), pages 203-215.
    10. Jeihoonian, Mohammad & Kazemi Zanjani, Masoumeh & Gendreau, Michel, 2016. "Accelerating Benders decomposition for closed-loop supply chain network design: Case of used durable products with different quality levels," European Journal of Operational Research, Elsevier, vol. 251(3), pages 830-845.
    11. Sushil R. Poudel & Md Abdul Quddus & Mohammad Marufuzzaman & Linkan Bian & Reuben F. Burch V, 2019. "Managing congestion in a multi-modal transportation network under biomass supply uncertainty," Annals of Operations Research, Springer, vol. 273(1), pages 739-781, February.
    12. Simon Thevenin & Yossiri Adulyasak & Jean-François Cordeau, 2022. "Stochastic Dual Dynamic Programming for Multiechelon Lot Sizing with Component Substitution," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3151-3169, November.
    13. Weiguo Zhang & Xiaolei He, 2022. "A New Scenario Reduction Method Based on Higher-Order Moments," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1903-1918, July.
    14. Poudel, Sushil Raj & Marufuzzaman, Mohammad & Bian, Linkan, 2016. "A hybrid decomposition algorithm for designing a multi-modal transportation network under biomass supply uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 1-25.
    15. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2016. "The impact of operations anticipations on the quality of stochastic location-allocation models," Omega, Elsevier, vol. 62(C), pages 19-33.
    16. Snyder, Lawrence V. & Daskin, Mark S. & Teo, Chung-Piaw, 2007. "The stochastic location model with risk pooling," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1221-1238, June.
    17. Ouhimmou, Mustapha & Nourelfath, Mustapha & Bouchard, Mathieu & Bricha, Naji, 2019. "Design of robust distribution network under demand uncertainty: A case study in the pulp and paper," International Journal of Production Economics, Elsevier, vol. 218(C), pages 96-105.
    18. Marufuzzaman, Mohammad & Eksioglu, Sandra D. & Li, Xiaopeng & Wang, Jin, 2014. "Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 122-145.
    19. Kabli, Mohannad & Quddus, Md Abdul & Nurre, Sarah G. & Marufuzzaman, Mohammad & Usher, John M., 2020. "A stochastic programming approach for electric vehicle charging station expansion plans," International Journal of Production Economics, Elsevier, vol. 220(C).
    20. Huang, Mingzhong & He, Junliang & Yu, Hang & Yan, Wei & Tan, Caimao, 2024. "Improved Benders decomposition for stack-based yard template generation in an automated container terminal," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:199:y:2009:i:2:p:409-419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.